Setting Up Your Development Environment

Before you start coding, ensure that Python is installed on your computer. Additionally, you will need to set up several libraries, including ibapi, ccxt, pandas, and binance. These libraries are essential for communicating with the TWS API, retrieving historical data, and managing account balances.

import ibapi

import queue

from ibapi.client import *

from ibapi.wrapper import *

import threading

from math import *

import pandas as pd

import ccxt

import numpy as np

import time

from binance.client import Client

from ibapi.contract import Contract

from ibapi.order import Order

from ibapi.client import EClient

from ibapi.wrapper import EWrapper

Connecting to the TWS API

The first step in creating our trading bot is to connect to the TWS API. We accomplish this by defining a custom class that inherits from EWrapper and EClient, enabling us to manage server responses and send requests.

class IBapi(EWrapper, EClient):

def __init__(self):

EClient.__init__(self, self)

Creating Order and Contract Objects

Next, we define functions that create order and contract objects. These functions will specify various parameters including the symbol, security type, exchange, currency, quantity, and price.

def make_contract(symbol, sec_type, exch, prim_exch, curr):

contract = Contract()

contract.m_symbol = symbol

contract.m_secType = sec_type

contract.m_exchange = exch

contract.m_primaryExch = prim_exch

contract.m_currency = curr

return contract

def make_order(action, quantity, price=None):

order = Order()

if price is not None:

order.m_orderType = 'LMT'

order.m_totalQuantity = quantity

order.m_action = action

order.m_lmtPrice = price

else:

order.m_orderType = 'MKT'

order.m_totalQuantity = quantity

order.m_action = action

return order

Retrieving Historical Price Data

To make educated trading decisions, it's important to gather historical data. We use the ccxt library to obtain historical price information for a selected financial instrument, such as Bitcoin (BTC) against the US Dollar (USD), from the Binance exchange.

client = Client()

symbol = "BTCUSDT"

klines = client.get_historical_klines(symbol, Client.KLINE_INTERVAL_1DAY, "80 days ago UTC")

df = pd.DataFrame(klines, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore'])

Implementing Trading Strategies

In this segment, we will implement a straightforward trading strategy based on pivot points. We calculate pivot points and support/resistance levels using historical price data and establish buy and sell conditions based on these levels.

df['close1'] = df['close'].shift(1)

df['open1'] = df['open'].shift(1)

Managing Account Balances

Effective account management is vital in algorithmic trading. We will use the TWS API to retrieve account balances and ensure there are adequate funds (USD) or assets (BTC) available for trading.

ib_api = IBapi()

ib_api.connect("127.0.0.1", 7497, clientId=1)

Executing Buy and Sell Orders

Based on our trading strategy and account balances, we will implement logic to execute buy and sell orders through TWS. This includes specifying order types (market or limit) and dynamically adjusting stop-loss levels to manage risk.

if x == 1:

if Portfolio > 10:

conn = Connection.create(port=7496, clientId=999)

conn.connect()

oid = 1

cont = make_contract('BTC', 'STK', 'SMART', 'SMART', 'USD')

offer = make_order('BUY', 1, 200)

conn.placeOrder(oid, cont, offer)

conn.disconnect()

print("OPEN A LONG", actualPrice)

sl = df.iloc[-2]['close'] * 0.97

time.sleep(5)

else:

pass

Error Handling and Retry Mechanism

To ensure the robustness of our bot, we will implement error handling to catch exceptions and a retry mechanism to attempt failed operations after a brief delay.

try:

# (Your code block here...)

except Exception as e:

print(f"An error occurred: {str(e)}")

time.sleep(300) # Retry after 5 minutes